Many recent advances in wireless transmission have rested on the use of multiple antennas for transmission and reception. Multiple antennas, fundamentally, can provide an increase in the numbers of Degrees of Freedom (DoFs) that can be exploited by a wireless system for transmission, i.e., the number of scalar data streams that can be simultaneously transmitted to the receiving parties in the system. DoFs can be used to provide increased spectral efficiency (throughput) and/or added diversity (robustness). Indeed, a Single User MIMO (SU-MIMO) system with Nt transmission (TX) antennas serving a single user with Nr receive (RX) antennas may be able to exploit up to min(Nt, Nr) DoFs for downlink transmission. These DOFs, for example, can under certain conditions be used to improve throughput by a factor that grows linearly with min(Nt, Nr). Such benefits of MIMO, and increased DoFs, underlie much of the interest in using MIMO in new and future systems.
Exploiting such DoFs often requires some amount of cost to the system. One such cost is knowledge of the channel state between transmitting and receiving antennas. Such Channel State Information (CSI) often has to be available to either the transmitter (such CSI is termed CSIT) and/or to the receiver (such CSI is termed CSIR). The DoFs available also depend on having sufficient “richness” in the channels between transmitting and receiving antennas. For example, SU-MIMO CSIR-based systems such as Bit Interleaved Coded Modulation (BICM) and D-BLAST can achieve the maximum possible DOFs of min(Nt, Nr) under suitable channel conditions. CSIT is not required. Under such conditions, they therefore can be used to provide corresponding linear increases in spectral efficiency. Such designs are well understood by those familiar with the state of the art.
Similarly, a Multi-User MIMO (MU-MIMO) system with Nt transmission antennas at the base station (BS) and K single-antenna users (Nr=1) can provide up to min(Nt, K) DoFs. As in the case of SU-MIMO, MU-MIMO can, for example, be used to improve throughput linearly with min(Nt, K). However, unlike SU-MIMO, many MU-MIMO techniques (in fact most if not all of the prevailing MU-MIMO techniques used and studied for standards) require knowledge of CSIT. MU-MIMO based on CSIT, unlike SU-MIMO based on CSIR, requires additional overheads to estimate CSI and feedback CSI to transmitters before the transmission can take place.
Despite such overheads, MU-MIMO is of practical interest since it has the benefit over SU-MIMO of being able to grow the DoFs without having to add many receive antennas, radio frequency (RF) chains, or increase processing (e.g., decoding) complexity to portable or mobile devices.
The issue of CSI overhead has to be considered carefully. It is a fundamental issue often overlooked in assessing such conventional MIMO. Such CSI-related overheads in fact can represent a fundamental “dimensionality bottleneck” that can limit the net spectral efficiency increase that can be obtained with conventional CSI-dependent MIMO. In particular, if one wants to continue to exploit the growth in DoFs (e.g., linear growth) by increasing Nt (or Nr or K), one also has to consider how to support increased system overhead in obtaining the CSI required to formulate transmissions and decode at the receivers. Such overhead can include increased use of the wireless medium for pilots supporting CSI estimation and increased feedback between receiving and transmitting entities on such CSI estimates.
As an example, assume that for each complex scalar value that defines the CSI between a single TX antenna and a single RX antenna (this type of CSI is often termed direct CSI by some in the Standards community) a fixed percentage Fcsi of wireless-channel resources is dedicated to pilots and/or feedback. One can see that as the dimension of the CSI required scales with quantities like Nt, Nr and/or K, the total CSI system-related overhead grows (e.g. by Nt×Fcsi). For example, for K single antenna users, each with Nt CSI scalar terms with respect to the transmitting antenna, there are a total of KNt such complex scalar values that the transmitter may need to know. Supporting an increase in the dimension of the CSI can take more wireless-channel resources, and reduces the amount of resources left for data transmission. This overhead increase can limit continued growth in throughput if spectral efficiency improvements do not offset increased CSI overheads.
The value Fcsi is often defined either by the system or by necessity given the coherence of channels in time and/or frequency. As the state of channels changes more rapidly in time and/or frequency, a larger effective fraction of resources may need to be used to estimate and keep track of CSI.
As an example, in a Frequency Division Duplex (FDD) based 3GPP Long Term Evolution (LTE) design, 8 symbols in a resource block of 12×14 OFDM symbols are used to support downlink pilots for each of the Nt antennas. Simply considering system overheads for such pilots, and ignoring other CSI related overheads such as feedback, Fcsi can be as large as 8/168=4.76%. It means that with Nt=8, assuming the pilot structure scales linearly with additional antennas, the total CSI-overhead could be as large as 38%, leaving 62% of symbols for supporting the remaining signaling overheads and data transmission. In fact, LTE has considered to change the pilot structure beyond Nt=4 antennas. However, this also has implications to CSI accuracy. Nonetheless, clearly, such a system would not support unbounded increases in Nt.
Thus, though symbols that represent coded data information are used more efficiently, with increased robustness and/or spectral efficiency due to the increased DoFs by MIMO, the net spectral efficiency increases have to account for the fraction of resources used for CSI overhead. Thus, the net spectral efficiency growth is in fact less than that of individual data symbols as only a fraction, e.g. no more than (1−Nt×Fcsi), of symbols can be used for data.
Recently a new class of techniques, termed “Blind Interference Alignment” (BIA) techniques, has demonstrated the ability to grow DoFs without requiring many of the CSI overheads of conventional MU-MIMO systems. It is possible for a BIA Multi-User MIMO (MU-MIMO) system with Nt transmission antennas at the BS and K single active-antenna users to achieve KNt/K+Nt−1) DoFs without CSIT. Thus, as K grows, the system can approach the CSI-dependent upper bound of min(Nt,K) DoFs that is achievable by conventional MU-MIMO CSIT-based systems. This is a striking result since it goes against much of the conventional thinking and conjectures over recent decades, and it provides the potential to relieve the “dimensionality bottleneck” being faced by current systems.
For such a system to work, there is a requirement that the channels seen between the transmitting BS and the K users being served must be jointly changing in a predetermined way (with respect to the blind interference alignment scheme). This joint variation can be accomplished by having multiple antenna modes. This can be implemented by employing many (physical) antenna elements at each user, or by having a single antenna element that can change its physical characteristic, e.g. orientation, sensitivity pattern, etc. However, in all such cases, the system requires only that one mode be active at a given time slot. Thus, it is sufficient to have only a single RF chain at each mobile, whereby the single active-receive antenna mode of a user i.e., the antenna driving the single RF chain of the user, can be varied over time. In other words, the single active receive antenna is a multi-mode antenna, able to switch between, e.g., Nt modes in a pre-determined fashion. Having a single RF chain keeps decoding complexity in line with conventional single-antenna mode MU-MIMO systems.
The modes must be able to create independent (e.g., linearly independent) CSI vectors for the single user. Transmission also has to be confined to a suitable coherence interval in time over which the CSI in a given mode, though unknown to the system, is assumed to be effectively constant and different from mode to mode.
The BIA technique works by creating a suitable antenna mode switching and combined data transmission vector over the K information bearing streams that are to be sent to the K users (one stream carries the intended information for one user). Such information bearing stream themselves are vectors. These are sent in various arithmetic combinations simultaneously thus using the extra DoFs provided by the antenna mode switching.
The coordination of user receive-antenna switching modes and the way the information streams are sent by the BIA scheme is designed to maximize the DoFs by complying with the following principles:                any Nt dimensional symbol intended for a given user is transmitted through Nt slots;        during these Nt slots, the antenna-switching pattern of that user ensures that the user observes that symbol through all its Nt antenna modes (thereby in an Nt dimensional space) and can thus decode it; and        in contrast, the antenna-switch patterns of the rest of the users are such that the transmission of this Nt dimensional symbol only casts an 1-dimensional shadow to their receivers. This is accomplished by ensuring that each of these receivers uses the same antenna mode in all the Nt dimensional symbol is transmitted.        
Thus, a total of (Nt+K−1) receiver dimensions are needed per user to decode Nt scalar symbols. As a result, with this scheme, K users decode a total of KNt symbols (Nt each) per (Nt+K−1) channel uses, thereby achieving the maximum possible BIA DoF of KNt/(Nt+K−1).
BIA techniques do have some inherent challenges and limitations in the scenarios in which they can be used. The first inherent problem is that BIA schemes often require high Signal to Noise Ratios (SNRs) to operate effectively, e.g. the original BIA scheme may require up to 20 dB of SNR. This is due to a property of the interference alignment process, which results in noise being amplified in the resulting interference-aligned streams. As a consequence of this, the original BIA technique has limited application to many users in a cellular environment. For example, cell-edge users in conventional cellular often experience Signal-to-Interference-plus-Noise-Ratios (SINRs) on the order of 0 dB or less, due to the interference coming from interfering cells not serving the K users, thus making it for the purpose of analysis effectively noise. Many users, not just cell-edge users, do not have SINRs on the order of 20 dB or more. Unfortunately, it is often the lower SNR users that are often the ones that need techniques to help them boost their spectral efficiency and DOFs. The BIA scheme therefore requires modification and a proper deployment setup to enable it to be useful to many users in a cellular environment.